Optical character recognition (OCR), or text recognition is used to translate text from paper to digital documents that can be easily searched, shared, and stored. Traditional OCR techniques work for printed text such as text generated by a printer or typewriter, but can fail when confronted by handwritten text, or text that is in a script with connected characters, such as when writing in cursive English or standard Arabic.
Thus, a need still remains for effective text recognition of all kinds of text and not just printed English. In view of the rapid rise of the global economy, it is increasingly critical that answers be found to these problems. In view of the ever-increasing commercial competitive pressures, along with growing consumer expectations and the diminishing opportunities for meaningful product differentiation in the marketplace, it is crucial that answers be found for these problems. Additionally, the need to reduce costs, improve efficiencies and performance, and meet competitive pressures adds an even greater urgency to the critical necessity for finding answers to these problems.
Solutions to these problems have been long sought but prior developments have not taught or suggested any solutions and, thus, solutions to these problems have long eluded those skilled in the art.